STAT 313: Applied Experimental Design and Regression Models
Winter 2023
1 Instructor Information
Dr. Allison Theobold
- Email: atheobol@calpoly.edu
- Office: Building 25 Office 105 (by Statistics Department Office)
2 Course Info
Room: Baker Science (Building 180), Room 272
Times:
- Section 01: 2:10-4:00pm
- Section 02: 4:10-6:00pm
3 Office Hours
| Day | Time |
|---|---|
| Tuesdays | 1:00 pm – 2:30 pm (in-person) |
| Wednesdays | 2:00 pm - 3:00 pm (in-person) |
| Thursdays | 1:00 pm – 2:30 pm (in-person) |
I am available for individual appointments on Wednesdays from 3:00 to 3:30pm. Appointments to meet must be made made at least 24 hours in advance through Calendly, using the following link: https://calendly.com/allisontheobold
4 Required Materials
For this course we will be using one main textbook, accompanied by additional resources. The textbooks we are using are free, but have the option to obtain a printed copy if you wish.
4.1 Textbooks
4.2 Required Technology
R is the statistical software we will be using in this course (https://cran.r-project.org/)
R software. We will be interacting with Posit through Posit Cloud (https://Posit.cloud/). You will join the Stat 313 workspace, and then be able to access the course homework and lab assignments. We will be walking through this in the first week of lab!
5 Welcoming Classroom
I value diversity, inclusion and equity in this (and every) class. I hold the fundamental belief that everyone is fully capable of learning and doing statistics. There is more than one way to address a statistical problem, and our learning will be richer by being open to different ideas, rejecting stereotypes, and being aware of—in order to minimize—our biases. I look forward to getting to know you all as individuals and as a learning community.
6 Course Description and Learning Objectives
Catalog Description: Applications of statistics for students not majoring in statistics or mathematics. Analysis of variance including one-way classification, randomized blocks, and factorial designs; multiple regression, model diagnostics, and model comparison. Prerequisite: Stat 217, Stat 218, Stat 221 or Stat 312
6.1 Learning Targets
Data Visualization & Summarization
create visualizations for one and two numerical variables
use facets and / or color to include additional variables into a visualization
calculate numerical summaries of variables
find summaries of variables across multiple groups
Working with Data & Reproducibility
select necessary columns from a dataset
filter rows from a dataset for numerical and categorical variables
modify existing numerical and categorical variables and / or create new variables
create professional-looking, reproducible analyses using Posit projects, Quarto documents, and the here package
Linear Models & Model Selection
explain why simple linear regression (SLR), multiple linear regression (MLR), and analysis of variance (ANOVA) are all members of the linear model family
identify which linear model is appropriate for a given research question
describe the conditions required to obtain reliable estimates from linear models
use visualizations, summary statistics, and critical thinking to evaluate if linear model conditions are violated
identify methods to remedy condition violations
fit additive and interactive linear models in R
interpret the coefficient estimates of a linear model
use visualizations and model selection techniques to determine if a specific variable should be included in a model
Study Design
distinguish between an experiment and an observational study
identify sources of variation and describe how to account for them
explain differences in sampling methods and contrast the inferences they permit
argue what population a given sample is representative of
Fundamentals of Statistical Inference
identify the parameter of interest for a given linear model and associated research question
outline the null (\(H_0\)) and alternative (\(H_A\)) hypotheses for a given research question
describe what a null distribution is and how it is used to obtain a p-value
interpret a p-value in the context of a research question
use a p-value to make a decision about a hypothesis test and reach a conclusion about a research question
distinguish between Type I and Type II errors
describe how sample size, significance level, and sampling variability effect Type I and Type II errors
outline the strengths and weaknesses of significance testing
describe what a bootstrap distribution is and how it is used to obtain a confidence interval
interpret a confidence interval in the context of the parameter of interest
describe the connection between confidence intervals and hypothesis testing
7 Course Organization
This class is organized into six units. The skills learned at the beginning of the course will carry throughout the remainder of the course. I hope that you are able to see the connections between the topics of the different units, since they are all part of one big family—the regression family!
7.1 Unit 1: Foundations of Statistics (Week 1)
This introductory unit has three big tasks, (1) review statistical and data oriented concepts you have (likely) seen before, (2) think critically about why statistics is used in science, and (3) think about how (historically) statistics has been used for inference.
Reading: Chapters 1 and 2 in Introduction to Modern Statistics (IMS), with supplementary articles
7.2 Unit 2: Exploratory Data Analysis (Weeks 2 & 3)
This unit focuses on building skills for working with and visualizing different types of data. First, we will focus on categorical data–creating summary tables and barcharts. Next, we will turn our attention to numerical data–calculating summary statistics, histograms, scatterplots, and linegraphs.
This unit will pair:
- Chapter 5 in IMS with Chapter 2 (sections 1-7) in Modern Dive
- Chapter 4 in IMS with Chapter 2 (section 8) in Modern Dive
7.3 Unit 3: Regression Modeling (Weeks 4 & 5)
In this unit we finally begin exploring statistical methods. You will put the tools you learned for wrangling and visualizing to work in the context of linear regression. We will start in a (likely) familiar context–linear regression. Once we’ve explored the concepts of “simple” / basic regression we will turn up the heat and add some additional explanatory variables with multiple linear regression.
This unit will pair:
- Chapter 7 in IMS with Chapter 5 in Modern Dive
- Chapter 8 in IMS with Chapter 6 in Modern Dive
7.4 Unit 4: Foundations of Statistical Inference (Weeks 6 & 7)
This unit will start with Chapter 7 in Modern Dive, setting the stage for why Statisticians care so much about variability. We will then use these ideas to walk through chapters 11 through 13 in Introduction to Modern Statistics, exploring different methods for summarizing the variability we might expect to see in different samples.
We will use these avenues to explore concepts you have seen before: hypothesis tests and confidence intervals. However, the main focus of these concepts will be on the idea of sampling variability not significance testing. We will visit the ideas of p-values and significance testing, with a emphasis on making (and justifying) sound scientific decisions.
7.5 Unit 5: Inference for Regression (Week 8 & 9)
Now that we’ve discussed the ideas behind sampling variability and statistical inference, we’ll explore these ideas in the context of linear regression. We will explore simple linear regression first, looking at how we can assess how “good” of a job our explanatory variable does in explaining the response variable. Next we’ll discuss how we can extend these ideas to a regression with multiple explanatory variables–using model selection criteria.
This unit will explore:
- Chapter 24 in IMS with Chapter 10 in Modern Dive
- Chapter 25 in IMS
7.6 Unit 6: ANOVA a (Boring) Case of Regression (Week 10)
To wrap up the quarter, we will look at a special case of linear regression–ANOVA. In this special case, our regression will include only categorical variables as explanatory variables. We will first review how we compare the means of two groups and then connect with what we learned about categorical variables in multiple linear to conceptualize how we can compare the means of three or more groups.
This unit will explore Chapter 21 and 22 in IMS
8 Course Components
Canvas will be your resource for the course materials necessary for each week. There will be a published “coursework” page which you can access through RStudio Connect. The page will walk you through what you are expected to do each week, including:
- textbook reading
- lecture videos
- homework questions
- quiz questions
Discord will be your resource for course discussions and questions.
Posit Cloud will be your resource for course lab assignments
- data sets
- lab assignments
- group projects
- software resources
Zoom will be used for the midterm and final oral exams.
8.1 Communication
Every Sunday evening there will be an announcement on Canvas letting you know what is due over the next week, and the material we will be covering. The module for each week will be released on Sunday evening, so you can look over the content and see what the plan is for the week.
We will use Discord to manage questions and responses regarding course content. There are channels for the different components of each week (e.g., Week 1 Lab Assignment). Please do not send an email about homework questions or questions about the course material. It is incredibly helpful for others in the course to see the questions you have and the responses to those questions. I will try to answer any questions posted to Discord within 3-4 hours (unless it is posted at midnight). If you think you can answer another student’s question, please respond!
Generally, I will work either Saturday or Sunday each weekend, and will work from approximately 7am to 5pm during the week. I will attempt to respond to emails in 24 hours, but emails sent on a Saturday night may not be responded to until Monday morning. If you don’t hear back from me in 48 hours, assume I did not receive your email and resend it!
9 Weekly Schedule
Each week in STAT 313 will, for the most part, look something like this:
Individual Expectations
Group Expectations
9.1 Individual Assignments
Readings and Videos
I favor a “flipped classroom,” since I believe it give you more hands on experiences working through the concepts we are learning. Thus, you should expect to dedicate time early in the week to reading the course material and watching the necessary videos.
Weekly Quizzes (Due Fridays at midnight)
Each week there will be a short (~10 questions) quiz over the reading and videos from the week. These quizzes are intended to ensure that you grasped the key concepts from the week’s readings. The quizzes are not timed, so you can feel free to check your answers with the textbook and/or videos if you so wish. You can attempt the quizzes three times before the deadline!
Tutorials
On the lab assignment weeks, I will assign a set of tutorials focusing on specific skills in R. These tutorials provide a review of the concepts covered in the textbook, give examples of how to work with data in R, and have hand-on exercises where you will need to write the R code necessary to complete a given task (with hints provided).
The tutorials are work at your own pace, so you can complete them all at once or slowly throughout the week. The lab assignments will require for you to put the skills you learned in the tutorials to work, so completing the tutorial is necessary to complete the R code in each lab assignment.
Statistical Critiques
Midterm & Final Projects
There will be two projects throughout the quarter, where you will be asked to apply the statistical concepts you have learned in the context of real data. Each of these projects will be done in the teams you have been working with in class. More details will be provided during class.
9.2 Team-based Assignments
Lab Assignments (Due Sundays at midnight)
Labs will be assigned approximately every other week, providing the opportunity to explore the course concepts in the context of real data. Lab assignments will require for you to work through the tutorial for the week, thus the tutorials should be started early!
You will complete the lab assignments in the same teams you collaborate with in class. You will access the lab assignment through Posit Cloud, which you will be walked through during the first lab. Your group will be expected to submit your completed lab on Canvas. You will need to submit both the HTML and RMarkdown documents.
Lab Assignment Grading
I expect that you will approach each lab assignment seriously, investing the necessary time and energy to prepare your responses. Different from what you may have experienced, lab assignments are graded for “mastery” of the concepts. The degree to which you “mastered” each question is assessed with the following four-point scale.
| Score | Justification |
|---|---|
| Successful | The solution to the problem is correct, legible, and easy to follow, with all reasoning provided. Any error is trivial. |
| Growing | The solution shows growth toward mastering the topic; however, it is incorrect and/or incomplete. |
| Not Assessable | Solution is missing or insubstantial, or the solution was attempted using an inappropriate methodology for the problem type. |
Each Lab Assignment is aligned to multiple learning targets, which describe what you should be able to do after taking this course. You’ll receive a score for each problem on an assignment according to the SGN rubric above, as well as feedback to help you improve.
After the first submission, you will have the option to retry any problems for which you scored a G. A written reflection on how your understanding of the problem changed will accompany any revision. If you receive an N on a problem, or if you don’t earn an S by the second try, you can make an appointment with me to meet during my office hours (or another agreed-upon time) to create a reassessment strategy. You can schedule up to one meeting per week.
You can submit up to one revised Lab Assignment per week, until Sunday, March 19 at 11:59 pm.
Lab Assignment Schedule
| Lab | Week |
|---|---|
| Lab 1 - Getting Started with RMarkdown | Week 1 |
| Lab 2 - Summarizing & Visualizing Categorical Variables | Week 2 |
| Lab 3 - Summarizing & Visualizing Numerical Variables | Week 3 |
| Lab 4 - Linear Regression in R | Week 4 |
| Lab 5 - Using the infer package for Inference | Week 7 |
| Lab 6 - Inference for Regression | Week 8 |
| Lab 7 - Model Selection | Week 9 |
10 Working in Teams
10.1 Team Member Roles
Your team will be rotating group roles each week, so that one person does not act as the “team manager” for more than one week. Instead the following roles will circulate each week, so that each member of the group is able to complete each role.
| Role | Responsibilities |
|---|---|
| Manager | Responsible for organizing the team work: making sure all roles were assigned and clear, scheduling meetings, and leading discussion of lab assignment problems. During the group discussions the editor is responsible for making sure everyone has a chance to contribute, asking quiet team members to speak up, asking loud team members to listen to others, and bringing the conversation back to the lab assignment if it deviates. |
| Recorder | Responsible for collecting, organizing, and recording answers to the assignment during the discussions, compiling the summary of the answers discussed, sending summary to editor. |
| Editor | Responsible for reviewing the draft summary provided by the case reporter, sharing the summary with the team, soliciting feedback from the team, and submitting the final assignment by the deadline. |
| Clarifier | During the team meeting the clarifier should assist the group by paraphrasing the ideas presented by other group members, e.g. “Let me make sure I understand…”. The clarifier is responsible for making sure that everyone in the group understands the solutions to the problems. |
There will be confidential peer evaluations completed every two weeks. I will use these to check-in on each group’s dynamics and ensure that everyone feels their voice is being heard.
10.2 Team Meetings
There will be time in each class for your team to work on the assignment for the week, however this time will not be sufficient to complete the assignments. Therefore, every group is expected to meet for at least 2-hours outside of class.
If you are not in attendance for more than one of your team meetings that week, you will be expected to complete that week’s assignment on your own.
My hope is that each member of the group looks over the week’s assignment on Monday while reading the week’s chapter(s). Then, each member should have some initial ideas to propose by Tuesday’s team meeting. During this meeting the reporter can begin the process of writing up the group’s ideas. The reporter will then provide the editor with their summary of the group’s ideas. As a team, you can choose to work together or independently on the assignment’s problems between course meetings. The manager is responsible for scheduling additional meetings, so that the editor is able to submit the assignment on time.
11 Grade Breakdown
Your grade in STAT 313 will contain the following components.
Note: If you have more than three “0” grades due to turning assignments in late, un-revised “Rs,” failure to participate in group collaborations, or missed assignments, the highest grade you will earn for the class is a C-.
12 Other Policies
12.1 What if I need to turn something in late?
Assignments and redos are expected to be submitted on time. However, every student will be permitted to submit one individual assignment 24 hours late without question. You do not need to contact me to use this allowance, but if you find yourself in a position where you have used this allowance and you cannot complete another assignment by the due date, you are expected to email me. Once you email me, we can work together to find a deadline that is fair to both you and other students. If I do not hear from you, I will take a 5% reduction in score for every day an assignment is late, up to four days
12.2 What if I need to miss class?
I encourage you to attend every class session, but policies are for narcs. I put a great deal of time into making each class session engaging and worth your time. Attendance in this course is not explicitly required, but it degrades your team’s trust in you when they don’t see you in class.
Here’s what you should do if you do miss a class:
- Talk to a classmate to figure out what information you missed
- Check Canvas for any necessary handouts or changes to assignments
- Email me with any questions you have after reviewing notes and handouts
If you miss a bunch of classes, please come talk to me. I’m working from the assumption that you care and are trying, but something is getting in your way (health issues? depression / anxiety? college stress?); let’s figure out what that is and how I can help.
12.3 What if I have accommodations or feel that accommodations would be beneficial to my learning?
I enthusiastically support the mission of Disability Resource Center to make education accessible to all. I design all my courses with accessibility at the forefront of my thinking, but if you have any suggestions for ways I can make things more accessible, please let me know. Come talk to me if you need accommodation for your disabilities. I honor self-diagnosis: let’s talk to each other about how we can make the course as accessible as possible. See also the standard syllabus statements, which include more information about formal processes.
12.4 How can I expect to be treated in this course?
Following Ihab Hassan, I strive to teach statistics so that people will stop killing each other. In my classroom, diversity and individual differences are respected, appreciated, and recognized as a source of strength. Students in this class are encouraged and expected to speak up and participate during class meetings, and to carefully and respectfully listen to each other. During the first few weeks of class, we’ll work together to create a set of norms that will govern our interactions with each other, to ensure that we’re always respectful of everyone.
So that everyone feels comfortable participating, every member of this class must show respect for every other member of this class. Any attitude or belief that espouses the superiority of one group of people over another is not welcome in my classroom. Such beliefs are directly destructive to the sense of community that we strive to create, and will sabotage our ability to learn from each other (and thus sabotage the entire structure of the course).
In summary: Be good to each other.
12.5 What consititutes plagiarism in a statistics class?
Paraphrasing or quoting another’s work without citing the source is a form of academic misconduct. This included the R code produced by someone else! Writing code is like writing a paper, it is obvious if you copied-and-pasted a sentence from someone else into your paper because the way each person writes is different.
Even inadvertent or unintentional misuse or appropriation of another’s work (such as relying heavily on source material that is not expressly acknowledged) is considered plagiarism. If you are struggling with writing the R code for an assignment, please reach out to me. I would prefer that I get to help you rather than you spending hours Googling things and get nowhere!
Any incident of dishonesty, copying or plagiarism will be reported to the Office of Student Rights and Responsibilities. Cheating or plagiarism will earn you a grade of N on the problem or assignment and will remove your ability to submit revisions for that assignment.
If you have any questions about using and citing sources, you are expected to ask for clarification.
For more information about what constitutes cheating and plagiarism, please see https://academicprograms.calpoly.edu/content/academicpolicies/Cheating.
12.6 I’m having difficulty paying for food and rent, what can I do?
If you have difficulty affording groceries or accessing sufficient food to eat every day, or if you lack a safe and stable place to live, and you believe this may affect your performance in the course, I urge you to contact the Dean of Students for support. Furthermore, please notify me if you are comfortable in doing so. This will enable me to advocate for you and to connect you with other campus resources.
12.7 My mental health is impairing my ability to engage in my classes, what should I do?
National surveys of college students have consistently found that stress, sleep problems, anxiety, depression, interpersonal concerns, death of a significant other and alcohol use are among the top ten health impediments to academic performance. If you are experiencing any mental health issues, I and Cal Poly are here to help you. Cal Poly’s Counseling Services (805-756-2511) is a free and confidential resource for assistance, support and advocacy.
12.8 Someone is threatening me, what can I do?
I will listen and believe you if someone is threatening you. I will help you get the help you need. I commit to changing campus culture that responds poorly to dating violence and stalking.